Automating accelerator tuning at GSI/FAIR

Automating accelerator tuning at GSI/FAIR | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Automating accelerator tuning at GSI/FAIR Sabrina Appel, Hendrik Alsmeier, Martin Bajzek, Oliver Boine-Frankenheim, and 19 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9236473/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Automated accelerator tuning at GSI and FAIR is achieved using the Geoff framework. Geoff provides real-time optimization of beam parameters and experimental setups, supporting fast deployment and control room integration. It significantly improves performance, reducing SIS18 synchrotron injection losses from 45% to 12% and speeding up fragment separator setup using a classification algorithms. Advanced optimization strategies, such as multi-objective and multi-fidelity Bayesian optimization, were applied to SIS18 injection tuning, while model-predictive control implemented via model-driven reinforcement learning allows fast, constraint-aware adaptation. Using dedicated ion-source setups of the PUMA experiment at TU Darmstadt, automated control and optimization algorithms were used to optimize a hot-cathode electron source and an multi-reflection time-of-flight mass spectrometer, demonstrating the feasibility of real-time tuning of ion sources and particle traps. Geoff’s modular design facilitates easy integration of classical and machine-learning-based algorithms, bridging traditional accelerator operations with modern data-driven optimization. Bayesian optimization BOBYQA beam injection SIS18 FRS traps accelerator tuning beam loss PUMA Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 02 May, 2026 Reviews received at journal 01 May, 2026 Reviews received at journal 30 Apr, 2026 Reviewers agreed at journal 29 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers invited by journal 07 Apr, 2026 Editor assigned by journal 27 Mar, 2026 Submission checks completed at journal 27 Mar, 2026 First submitted to journal 26 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9236473","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":619173220,"identity":"0e80a0ac-4cbc-46e0-a379-8bb933ddf0eb","order_by":0,"name":"Sabrina 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